On Smoothing and Inference for Topic Models.
, 2009.
A. Asuncion, M. Welling, P. Smyth and Y.W. Teh.
[doi]
[BibTeX]
A Generic Approach to Topic Models.
Machine Learning and Knowledge Discovery in Databases:517-532, 2009.
Gregor Heinrich.
[doi]
[abstract]
[BibTeX]
This article contributes a generic model of topic models. To define the problem space, general characteristics for this class
of models are derived, which give rise to a representation of topic models as “mixture networks”, a domain-specific compactalternative to Bayesian networks. Besides illustrating the interconnection of mixtures in topic models, the benefit of thisrepresentation is its straight-forward mapping to inference equations and algorithms, which is shown with the derivation andimplementation of a generic Gibbs sampling algorithm.
Named Entity Resolution Using Automatically Extracted Semantic Information.
, 2009.
A. Pilz, G. Paaß and G. St Augustin.
[doi]
[BibTeX]
Conference articles
On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking..
In:
ICDM, pages 3-12.
IEEE Computer Society, 2008.
Loulwah AlSumait, Daniel Barbará and Carlotta Domeniconi.
[doi]
[BibTeX]
Journal articles
Link-PLSA-LDA: A new unsupervised model for topics and influence of blogs.
, 2008.
R. Nallapati and W. Cohen.
[doi]
[BibTeX]
Topic and role discovery in social networks with experiments on enron and academic email.
Journal of Artificial Intelligence Research, 30:249-272, 2007.
A. McCallum, X. Wang and A. Corrada-Emmanuel.
[doi]
[BibTeX]
Miscellaneous
Latent Semantic Analysis: A Road to Meaning.
2007.
M. Steyvers and T. Griffiths.
[BibTeX]
Conference articles
Latent Dirichlet Co-Clustering.
In:
ICDM '06: Proceedings of the Sixth International Conference on Data Mining, pages 542-551.
IEEE Computer Society, Washington, DC, USA, 2006.
M. Mahdi Shafiei and Evangelos E. Milios.
[BibTeX]
Journal articles
Clustering with Bregman Divergences..
Journal of Machine Learning Research, 6:1705-1749, 2005.
Arindam Banerjee, Srujana Merugu, Inderjit S. Dhillon and Joydeep Ghosh.
[doi]
[BibTeX]
Book chapters
Integrating Topics and Syntax.
In:
L. K. Saul, Y. Weiss and Léon. Bottou, editors,
Advances in Neural Information Processing Systems 17, pages 537-544.
MIT Press, Cambridge, MA, 2005.
Thomas L. Griffiths, Mark Steyvers, David M. Blei and Joshua B. Tenenbaum.
[BibTeX]
Journal articles
Finding scientific topics.
Proceedings of the National Academy of Sciences, 101(Suppl. 1):5228-5235, 2004.
T. L. Griffiths and M. Steyvers.
[BibTeX]
Technical reports
Learning in graphical models.
2003.
Michael I. Jordan.
[BibTeX]
Journal articles
Variational extensions to EM and multinomial PCA.
Lecture notes in computer science:23-34, 2002.
W. Buntine.
[doi]
[BibTeX]
Probabilistic inference in graphical models.
Handbook of neural networks and brain theory, 2002.
M.I. Jordan and Y. Weiss.
[doi]
[BibTeX]
Miscellaneous
An introduction to graphical models.
Web. 2001.
Kevin Murphy.
[doi]
[BibTeX]
Journal articles
An Introduction to Variational Methods for Graphical Models.
Mach. Learn., 37(2):183-233, 1999.
Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola and Lawrence K. Saul.
[doi]
[BibTeX]
Latent variable models.
Learning in graphical models, 1998.
C.M. Bishop.
[doi]
[BibTeX]
Miscellaneous
Journal articles
Operations for Learning with Graphical Models.
Journal of Artificial Intelligence Research, 2:159-225, 1994.
Wray L. Buntine.
[doi]
[abstract]
[BibTeX]
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
Maximum likelihood from incomplete data via the EM algorithm.
JOURNAL OF THE ROYAL STATISTICAL SOCIETY, SERIES B, 39(1):1-38, 1977.
A. P. Dempster, N. M. Laird and D. B. Rubin.
[doi]
[abstract]
[BibTeX]
A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behaviour of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis.